We use the same error metrics as described in [1]} for the PPMI dataset. We use the normalised root mean squared error (eq:ednormalp) for Gaussian, log-normal, Poisson, and beta distributed variables. Moreover, for categorical values we make use of the accuracy error (eq:edcatp), and for ordinal values we make use of the displacement error (eq:edordinalp).
\(e_{\operatorname{numerical}} =\frac{\sqrt{\frac{1}{n} \sum _{n}\left(y_{n d}-\hat{y}_{n d}\right)^{2}} }{\operatorname{max}(\mathbf {y_d})-\operatorname{min}(\mathbf {y_d})}\) 
\(e_{\operatorname{cat}} = \frac{1}{n} \sum _{n} \mathbb {I}\left(y_{n d} \ne \hat{y}_{n d}\right) \) 
\(e_{\operatorname{ordinal}} = \frac{1}{n} \sum _{n} \frac{\left|y_{n d} - \hat{y}_{n d}\right|}{R} \) 
